# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2
 
# construct the argument parser and parse the arguments
#ap = argparse.ArgumentParser()
#ap.add_argument("-p", "--shape-predictor", required=True,
#	help="path to facial landmark predictor")
#ap.add_argument("-i", "--image", required=True,
#	help="path to input image")
#args = vars(ap.parse_args())


# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
#this file cab be download here http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
cap=cv2.VideoCapture(0)
while cv2.waitKey(1)!=27:

# load the input image, resize it, and convert it to grayscale
#image = cv2.imread(args["image"])
    ret,image=cap.read()
#image = imutils.resize(image, width=500)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
# detect faces in the grayscale image
    rects = detector(gray, 1)
    #print (rects)

# loop over the face detections
    for (i, rect) in enumerate(rects):
	# determine the facial landmarks for the face region, then
	# convert the facial landmark (x, y)-coordinates to a NumPy
	# array
        shape = predictor(gray, rect)
        shape = face_utils.shape_to_np(shape)
 
	# convert dlib's rectangle to a OpenCV-style bounding box
	# [i.e., (x, y, w, h)], then draw the face bounding box
        (x, y, w, h) = face_utils.rect_to_bb(rect)
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
	# show the face number
        cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
		cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
 
	# loop over the (x, y)-coordinates for the facial landmarks
	# and draw them on the image
        for (x, y) in shape:
            cv2.circle(image, (x, y), 1, (0, 0, 255), -1)
 
# show the output image with the face detections + facial landmarks
    cv2.imshow("Output", image)
#cv2.waitKey(0)
